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Developing in Agentic AI Systems (GH-600)
Description
This course is designed to build practical skills in developing, deploying, and managing agentic AI systems within GitHub-based software development workflows. The course explores how to integrate AI agents into the software development lifecycle (SDLC), including designing agent architectures, configuring tools and environments, and managing agent memory, state, and execution. Students will learn how to evaluate and optimize agent performance, implement governance and guardrails, and coordinate multi-agent systems to ensure safe, reliable, and efficient outcomes. Through hands-on learning, participants will gain the skills needed to operate, supervise, and govern AI agents in production environments using GitHub as the control plane.
Prerequisites
- A GitHub account
- Basic understanding of AI fundamentals
- Basic understanding of repositories, branches, and pull requests
- General knowledge of CI and CD concepts
Audience Profile
Learners should have subject matter expertise in operating, integrating, supervising, and governing AI agents inside production-grade SDLC workflows and development environments, ensuring reliability, safety, and velocity using GitHub as the system of record and control plane. Learners work closely with architects, platform engineers, DevOps engineers, application developers, product managers, and security engineers to develop, deploy, operate, and manage agents that operate within the GitHub platform. Learners should have experience with the software development lifecycle (SDLC), workflows in GitHub and controls, and code quality, security, and review practices. You should also have experience with coding agents including GitHub Copilot, MCP servers and agent customization such as custom instructions, custom agents, tools, and Copilot setup Responsibilities for this role include:
- Operating agent workflows inside the SDLC
- Supervising autonomous behavior with GitHub controls
- Evaluating and tuning agent outputs using scans and artifacts
- Configuring custom agents
- Coordinating multi-agent execution safely
Developing in Agentic AI Systems Course Outline
- Integrate AI agents into the software development lifecycle (SDLC) by defining agent tasks, inputs/outputs, and execution boundaries
- Design and configure agent architectures that separate planning, reasoning, and execution to improve reliability and control
- Implement tool use and environment interactions by configuring agent tools, permissions, and MCP servers within development environments
- Design reliable multi-agent systems in GitHub using observable workflows, coordinated artifacts, and safe recovery mechanisms
- Learn how to manage agent memory and state, persist progress across environments, and evaluate agent behavior using clear success signals
- Develop secure and compliant agent governance using GitHub-native controls, human-in-the-loop approvals, and least-privilege access
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$695.00
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1 Day Course |